replace_nullable_types

Transformer to replace features with the new nullable dtypes with a dtype that is compatible in EvalML.

Module Contents

Classes Summary

ReplaceNullableTypes

Transformer to replace features with the new nullable dtypes with a dtype that is compatible in EvalML.

Contents

class evalml.pipelines.components.transformers.preprocessing.replace_nullable_types.ReplaceNullableTypes(random_seed=0, **kwargs)[source]

Transformer to replace features with the new nullable dtypes with a dtype that is compatible in EvalML.

Attributes

hyperparameter_ranges

None

modifies_features

True

modifies_target

{}

name

Replace Nullable Types Transformer

training_only

False

Methods

clone

Constructs a new component with the same parameters and random state.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters.

fit

Fits component to data.

fit_transform

Substitutes non-nullable types for the new pandas nullable types in the data and target data.

load

Loads component at file path.

needs_fitting

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

parameters

Returns the parameters which were used to initialize the component.

save

Saves component at file path.

transform

Transforms data by replacing columns that contain nullable types with the appropriate replacement type.

clone(self)

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)

Describe a component and its parameters.

Parameters
  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)[source]

Fits component to data.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

  • y (pd.Series, optional) – The target training data of length [n_samples].

Returns

self

fit_transform(self, X, y=None)[source]

Substitutes non-nullable types for the new pandas nullable types in the data and target data.

Parameters
  • X (pd.DataFrame, optional) – Input features.

  • y (pd.Series) – Target data.

Returns

The input features and target data with the non-nullable types set.

Return type

tuple of pd.DataFrame, pd.Series

static load(file_path)

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)

Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

Saves component at file path.

Parameters
  • file_path (str) – Location to save file.

  • pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)[source]

Transforms data by replacing columns that contain nullable types with the appropriate replacement type.

“float64” for nullable integers and “category” for nullable booleans.

Parameters
  • X (pd.DataFrame) – Data to transform

  • y (pd.Series, optional) – Target data to transform

Returns

Transformed X pd.Series: Transformed y

Return type

pd.DataFrame